




"""simple docstring"""

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Dict     =      (DPMSolverSinglestepScheduler,)
 UpperCAmelCase       :	str     =      (('''num_inference_steps''', 25),)

 def      lowerCAmelCase_				(					self			:			str       ,				**_UpperCAmelCase			:			Any						):
       _A     =  {
           'num_train_timesteps': 1_000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'solver_order': 2,
           'prediction_type': 'epsilon',
           'thresholding': False,
           'sample_max_value': 1.0,
           'algorithm_type': 'dpmsolver++',
           'solver_type': 'midpoint',
           'lambda_min_clipped': -float('inf'						),
           'variance_type': None,
       }

       config.update(**_UpperCAmelCase						)
       return config

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Optional[Any]=0       ,				**_UpperCAmelCase			:			Optional[Any]						):
       _A     =  dict(self.forward_default_kwargs						)
       _A     =  kwargs.pop('num_inference_steps'       ,				_UpperCAmelCase						)
       _A     =  self.dummy_sample
       _A     =  0.1 * sample
       _A     =  [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             _A     =  self.get_scheduler_config(**_UpperCAmelCase						)
             _A     =  scheduler_class(**_UpperCAmelCase						)
             scheduler.set_timesteps(_UpperCAmelCase						)
             # copy over dummy past residuals
             _A     =  dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase						)
                   _A     =  scheduler_class.from_pretrained(_UpperCAmelCase						)
                   new_scheduler.set_timesteps(_UpperCAmelCase						)
                   # copy over dummy past residuals
                   _A     =  dummy_past_residuals[: new_scheduler.config.solver_order]

             _A  ,       _A     =  sample, sample
             for t in range(_UpperCAmelCase       ,				time_step + scheduler.config.solver_order + 1						):
                   _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample
                   _A     =  new_scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample

                   assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

 def      lowerCAmelCase_				(					self			:			Dict						):
       pass

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			List[Any]=0       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  dict(self.forward_default_kwargs						)
       _A     =  kwargs.pop('num_inference_steps'       ,				_UpperCAmelCase						)
       _A     =  self.dummy_sample
       _A     =  0.1 * sample
       _A     =  [residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
             _A     =  self.get_scheduler_config()
             _A     =  scheduler_class(**_UpperCAmelCase						)
             scheduler.set_timesteps(_UpperCAmelCase						)

             # copy over dummy past residuals (must be after setting timesteps)
             _A     =  dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase						)
                   _A     =  scheduler_class.from_pretrained(_UpperCAmelCase						)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_UpperCAmelCase						)

                   # copy over dummy past residual (must be after setting timesteps)
                   _A     =  dummy_past_residuals[: new_scheduler.config.solver_order]

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample
             _A     =  new_scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				**_UpperCAmelCase						).prev_sample

             assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Any=None       ,				**_UpperCAmelCase			:			str						):
       if scheduler is None:
             _A     =  self.scheduler_classes[0]
             _A     =  self.get_scheduler_config(**_UpperCAmelCase						)
             _A     =  scheduler_class(**_UpperCAmelCase						)

       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config(**_UpperCAmelCase						)
       _A     =  scheduler_class(**_UpperCAmelCase						)

       _A     =  10
       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						).prev_sample

       return sample

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
       _A     =  50
       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase						)

       # make sure that the first t is uneven
       for i, t in enumerate(scheduler.timesteps[3:]						):
             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						).prev_sample

       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       assert abs(result_mean.item() - 0.2574						) < 1E-3

 def      lowerCAmelCase_				(					self			:			int						):
       for timesteps in [25, 50, 100, 999, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int						):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       _A     =  DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
       _A     =  self.full_loop(scheduler=_UpperCAmelCase						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       assert abs(result_mean.item() - 0.2791						) < 1E-3

       _A     =  DEISMultistepScheduler.from_config(scheduler.config						)
       _A     =  DPMSolverMultistepScheduler.from_config(scheduler.config						)
       _A     =  UniPCMultistepScheduler.from_config(scheduler.config						)
       _A     =  DPMSolverSinglestepScheduler.from_config(scheduler.config						)

       _A     =  self.full_loop(scheduler=_UpperCAmelCase						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       assert abs(result_mean.item() - 0.2791						) < 1E-3

 def      lowerCAmelCase_				(					self			:			str						):
       self.check_over_configs(thresholding=_UpperCAmelCase						)
       for order in [1, 2, 3]:
             for solver_type in ["midpoint", "heun"]:
                   for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                               self.check_over_configs(
                                   thresholding=_UpperCAmelCase       ,				prediction_type=_UpperCAmelCase       ,				sample_max_value=_UpperCAmelCase       ,				algorithm_type='dpmsolver++'       ,				solver_order=_UpperCAmelCase       ,				solver_type=_UpperCAmelCase       ,				)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Any						):
       for algorithm_type in ["dpmsolver", "dpmsolver++"]:
             for solver_type in ["midpoint", "heun"]:
                   for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                               self.check_over_configs(
                                   solver_order=_UpperCAmelCase       ,				solver_type=_UpperCAmelCase       ,				prediction_type=_UpperCAmelCase       ,				algorithm_type=_UpperCAmelCase       ,				)
                               _A     =  self.full_loop(
                                   solver_order=_UpperCAmelCase       ,				solver_type=_UpperCAmelCase       ,				prediction_type=_UpperCAmelCase       ,				algorithm_type=_UpperCAmelCase       ,				)
                               assert not torch.isnan(_UpperCAmelCase						).any(), "Samples have nan numbers"

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       self.check_over_configs(lower_order_final=_UpperCAmelCase						)
       self.check_over_configs(lower_order_final=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       self.check_over_configs(lambda_min_clipped=-float('inf'						)						)
       self.check_over_configs(lambda_min_clipped=-5.1						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       self.check_over_configs(variance_type=_UpperCAmelCase						)
       self.check_over_configs(variance_type='learned_range'						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
             self.check_over_forward(num_inference_steps=_UpperCAmelCase       ,				time_step=0						)

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  self.full_loop()
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       assert abs(result_mean.item() - 0.2791						) < 1E-3

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  self.full_loop(use_karras_sigmas=_UpperCAmelCase						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       assert abs(result_mean.item() - 0.2248						) < 1E-3

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  self.full_loop(prediction_type='v_prediction'						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       assert abs(result_mean.item() - 0.1453						) < 1E-3

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  self.full_loop(prediction_type='v_prediction'       ,				use_karras_sigmas=_UpperCAmelCase						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       assert abs(result_mean.item() - 0.0649						) < 1E-3



 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config(thresholding=_UpperCAmelCase       ,				dynamic_thresholding_ratio=0						)
       _A     =  scheduler_class(**_UpperCAmelCase						)

       _A     =  10
       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter.half()
       scheduler.set_timesteps(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						).prev_sample

       assert sample.dtype == torch.floataa





from ..utils import DummyObject, requires_backends

class     A__   (       metaclass=snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['flax', 'transformers']
    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(self     ,   ['''flax''', '''transformers''']       )
    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )






class     A__   (       metaclass=snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['flax', 'transformers']
    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(self     ,   ['''flax''', '''transformers''']       )
    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )






class     A__   (       metaclass=snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['flax', 'transformers']
    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(self     ,   ['''flax''', '''transformers''']       )
    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )






class     A__   (       metaclass=snake_case__      ):







    """simple docstring"""

    __magic_name__         =						['flax', 'transformers']
    def __init__(			self     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(self     ,   ['''flax''', '''transformers''']       )
    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




    @classmethod
    def 						a_     (			cls     ,   *__snake_case     ,   **__snake_case       ):
         requires_backends(cls     ,   ['''flax''', '''transformers''']       )




